The reliabilities of the gas-path components (compressor, burners and turbines) of a gas
turbine (GT) are usually high when compared with those of other GT systems such as
fuel supply and control. However, in the event of forced outage, downtimes are
normally high, giving a relatively low availability.
The purpose of condition monitoring and fault diagnostics is to detect, isolate and assess
(i.e. estimate quantitatively the magnitude of) the faults within a system, which in this
case is the gas turbine. An effective technique would provide a significant improvement
in economic performance, reduce operational and maintenance costs, increase
availability and improve the level of safety achieved. However, conventional analytical
techniques such as gas-path analysis and its variants are limited in their applications to
engine diagnostics due to several reasons that include their inability to:- operate
effectively in the presence of noisy measurements; distinguish effectively sensor bias
from component faults; preserve the nonlinearity in the gas-turbine parameter
relationships; and the requirement for more sensors for achieving accurate diagnostics.
The novelty of this research stems from its objective of overcoming most of these
limitations and much more.
In this thesis, we present the approach adopted in developing a diagnostic framework
for the detection of faults in the gas-path of a gas turbine. The framework involves a
large-scale integration of artificial neural networks (ANNs) designed and trained to
detect, isolate and assess the faults in the gas-path components of the engine. Input to
the diagnostic framework are engine measurements such as spool speeds, pressures,
temperatures and fuel flow while outputs are either levels of changes in sensor(s) for the
case of sensor fault(s) or the level of changes in efficiencies and flow capacities for the
case of faulty components. The diagnostic framework has the capacity to assess both
multiple component and multiple sensor faults over a range of operating points. In the
case of component faults, the diagnostic system provides changes in efficiencies and
flow capacities from which interpretations can be sought for the nature of the physical
problem. The implication of this is that the diagnostic system covers a wide range of
problems - both likely and unlikely-.
The technique has been applied to several developed test cases, which are not only
thermodynamically similar to operational engines, but also covers a range of engine
configurations and operating conditions. The results obtained from the developed
approach has been compared against those obtained from linear and nonlinear (recursive
linear) gas-path analysis, as well as from the use of fuzzy logic. Analysis of the results
demonstrates the promise of ANN applied to engine gas-path fault diagnostic activities.
Finally, the limitations of this research and direction for future work are presented.